Overview

Dataset statistics

Number of variables10
Number of observations442
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.7 KiB
Average record size in memory80.3 B

Variable types

Numeric9
Categorical1

Alerts

s1 is highly correlated with s2 and 2 other fieldsHigh correlation
s2 is highly correlated with s1 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s1 and 3 other fieldsHigh correlation
s5 is highly correlated with s1 and 1 other fieldsHigh correlation
s1 is highly correlated with s2 and 2 other fieldsHigh correlation
s2 is highly correlated with s1 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s1 and 3 other fieldsHigh correlation
s5 is highly correlated with s1 and 1 other fieldsHigh correlation
s1 is highly correlated with s2High correlation
s2 is highly correlated with s1 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s2 and 1 other fieldsHigh correlation
s1 is highly correlated with s2 and 2 other fieldsHigh correlation
s2 is highly correlated with s1 and 1 other fieldsHigh correlation
s3 is highly correlated with s4High correlation
s4 is highly correlated with s1 and 3 other fieldsHigh correlation
s5 is highly correlated with s1 and 1 other fieldsHigh correlation

Reproduction

Analysis started2022-07-07 10:53:52.678802
Analysis finished2022-07-07 10:54:31.188417
Duration38.51 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct58
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.139770997 × 10-20
Minimum-0.1072256316
Maximum0.1107266755
Zeros0
Zeros (%)0.0%
Negative202
Negative (%)45.7%
Memory size3.6 KiB
2022-07-07T16:24:31.508517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1072256316
5-th percentile-0.0854304009
Q1-0.03729926643
median0.005383060374
Q30.03807590643
95-th percentile0.07076875249
Maximum0.1107266755
Range0.2179523071
Interquartile range (IQR)0.07537517286

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)1.516640789 × 1018
Kurtosis-0.6712236886
Mean3.139770997 × 10-20
Median Absolute Deviation (MAD)0.03632538451
Skewness-0.231381533
Sum-3.747002708 × 10-16
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:31.887790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0162806757319
 
4.3%
0.0417084448817
 
3.8%
0.00901559882516
 
3.6%
-0.0273097856815
 
3.4%
-0.00188201652814
 
3.2%
-0.0527375548414
 
3.2%
0.0453409833414
 
3.2%
0.0126481372814
 
3.2%
0.0671362140413
 
2.9%
0.00538306037413
 
2.9%
Other values (48)293
66.3%
ValueCountFrequency (%)
-0.10722563163
 
0.7%
-0.10359309323
 
0.7%
-0.099960554712
 
0.5%
-0.096328016254
0.9%
-0.09269547784
0.9%
-0.089062939353
 
0.7%
-0.08543040095
1.1%
-0.081797862452
 
0.5%
-0.0781653244
0.9%
-0.074532785558
1.8%
ValueCountFrequency (%)
0.11072667552
 
0.5%
0.096196521652
 
0.5%
0.09256398321
 
0.2%
0.088931444751
 
0.2%
0.08529890631
 
0.2%
0.081666367855
 
1.1%
0.078033829391
 
0.2%
0.074401290946
1.4%
0.070768752497
1.6%
0.0671362140413
2.9%

sex
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size33.4 KiB
-0.044641636506989144
235 
0.05068011873981862
207 

Length

Max length21
Median length21
Mean length20.06334842
Min length19

Characters and Unicode

Total characters8868
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.05068011873981862
2nd row-0.044641636506989144
3rd row0.05068011873981862
4th row-0.044641636506989144
5th row-0.044641636506989144

Common Values

ValueCountFrequency (%)
-0.044641636506989144235
53.2%
0.05068011873981862207
46.8%

Length

2022-07-07T16:24:32.268510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-07T16:24:32.658598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.044641636506989144235
53.2%
0.05068011873981862207
46.8%

Most occurring characters

ValueCountFrequency (%)
01533
17.3%
61354
15.3%
41175
13.2%
11091
12.3%
81063
12.0%
9677
7.6%
.442
 
5.0%
3442
 
5.0%
5442
 
5.0%
-235
 
2.6%
Other values (2)414
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8191
92.4%
Other Punctuation442
 
5.0%
Dash Punctuation235
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01533
18.7%
61354
16.5%
41175
14.3%
11091
13.3%
81063
13.0%
9677
8.3%
3442
 
5.4%
5442
 
5.4%
7207
 
2.5%
2207
 
2.5%
Other Punctuation
ValueCountFrequency (%)
.442
100.0%
Dash Punctuation
ValueCountFrequency (%)
-235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8868
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01533
17.3%
61354
15.3%
41175
13.2%
11091
12.3%
81063
12.0%
9677
7.6%
.442
 
5.0%
3442
 
5.0%
5442
 
5.0%
-235
 
2.6%
Other values (2)414
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01533
17.3%
61354
15.3%
41175
13.2%
11091
12.3%
81063
12.0%
9677
7.6%
.442
 
5.0%
3442
 
5.0%
5442
 
5.0%
-235
 
2.6%
Other values (2)414
 
4.7%

bmi
Real number (ℝ)

Distinct163
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.255611484 × 10-16
Minimum-0.0902752959
Maximum0.170555226
Zeros0
Zeros (%)0.0%
Negative247
Negative (%)55.9%
Memory size3.6 KiB
2022-07-07T16:24:33.032851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0902752959
5-th percentile-0.06656343027
Q1-0.03422906806
median-0.00728376621
Q30.03124801543
95-th percentile0.08540807214
Maximum0.170555226
Range0.2608305219
Interquartile range (IQR)0.06547708349

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-2.111136955 × 1014
Kurtosis0.09509447428
Mean-2.255611484 × 10-16
Median Absolute Deviation (MAD)0.03125655014
Skewness0.5981484879
Sum-9.946210522 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:33.486725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.024528759398
 
1.8%
-0.030995631848
 
1.8%
-0.0083615782847
 
1.6%
-0.046085000877
 
1.6%
-0.025606571477
 
1.6%
0.0013387303816
 
1.4%
0.0045721666036
 
1.4%
0.014272475276
 
1.4%
-0.02021751116
 
1.4%
-0.023450947326
 
1.4%
Other values (153)375
84.8%
ValueCountFrequency (%)
-0.09027529591
0.2%
-0.089197483821
0.2%
-0.084886235531
0.2%
-0.083808423461
0.2%
-0.081652799312
0.5%
-0.080574987231
0.2%
-0.079497175161
0.2%
-0.077341551012
0.5%
-0.076263738941
0.2%
-0.075185926861
0.2%
ValueCountFrequency (%)
0.1705552261
0.2%
0.16085491731
0.2%
0.13714305171
0.2%
0.12852055511
0.2%
0.1274427431
0.2%
0.12528711891
0.2%
0.12313149471
0.2%
0.11450899811
0.2%
0.11127556191
0.2%
0.11019774981
0.2%

bp
Real number (ℝ)

Distinct100
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.684538328 × 10-17
Minimum-0.1123988025
Maximum0.1320436167
Zeros0
Zeros (%)0.0%
Negative244
Negative (%)55.2%
Memory size3.6 KiB
2022-07-07T16:24:33.908985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1123988025
5-th percentile-0.07435529926
Q1-0.03665608108
median-0.005670422293
Q30.03564378942
95-th percentile0.08367156053
Maximum0.1320436167
Range0.2444424193
Interquartile range (IQR)0.07229987049

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-1.016515274 × 1015
Kurtosis-0.5327972701
Mean-4.684538328 × 10-17
Median Absolute Deviation (MAD)0.03442850976
Skewness0.2906583668
Sum-2.114974862 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:34.368484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0400989320521
 
4.8%
-0.00567042229321
 
4.8%
-0.0263275281520
 
4.5%
0.0218723855115
 
3.4%
-0.033213230114
 
3.2%
-0.0228846771713
 
2.9%
-0.0159989752211
 
2.5%
0.00810098161111
 
2.5%
-0.0125561242411
 
2.5%
0.0494151933211
 
2.5%
Other values (90)294
66.5%
ValueCountFrequency (%)
-0.11239880251
 
0.2%
-0.10895595161
 
0.2%
-0.10207024961
 
0.2%
-0.10093410881
 
0.2%
-0.098627398641
 
0.2%
-0.084855994744
0.9%
-0.081413143764
0.9%
-0.077970292791
 
0.2%
-0.074527441819
2.0%
-0.071084590831
 
0.2%
ValueCountFrequency (%)
0.13204361671
 
0.2%
0.12515791481
 
0.2%
0.10794365993
0.7%
0.10450080892
 
0.5%
0.1010579581
 
0.2%
0.09875124781
 
0.2%
0.097615106985
1.1%
0.094172256011
 
0.2%
0.090729405032
 
0.5%
0.087286554064
0.9%

s1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct141
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.306144735 × 10-17
Minimum-0.1267806699
Maximum0.1539137132
Zeros0
Zeros (%)0.0%
Negative240
Negative (%)54.3%
Memory size3.6 KiB
2022-07-07T16:24:34.768872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1267806699
5-th percentile-0.07311850845
Q1-0.0342478402
median-0.004320865537
Q30.02835801485
95-th percentile0.08367131975
Maximum0.1539137132
Range0.2806943831
Interquartile range (IQR)0.06260585505

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-3.645771127 × 1015
Kurtosis0.2329479047
Mean-1.306144735 × 10-17
Median Absolute Deviation (MAD)0.03095893931
Skewness0.3781082069
Sum-6.231126726 × 10-15
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:35.170241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.00707277125310
 
2.3%
-0.0373437341310
 
2.3%
0.012190568769
 
2.0%
0.020446285919
 
2.0%
0.0011829458968
 
1.8%
0.024574144498
 
1.8%
-0.024960158418
 
1.8%
-0.0043208655378
 
1.8%
-0.0029449126788
 
1.8%
-0.0098246769697
 
1.6%
Other values (131)357
80.8%
ValueCountFrequency (%)
-0.12678066991
0.2%
-0.10889328281
0.2%
-0.10476542421
0.2%
-0.10338947131
0.2%
-0.10063756561
0.2%
-0.096509707042
0.5%
-0.09100589561
0.2%
-0.089629942752
0.5%
-0.088253989891
0.2%
-0.086878037031
0.2%
ValueCountFrequency (%)
0.15391371321
0.2%
0.15253776031
0.2%
0.13327442031
0.2%
0.12777060892
0.5%
0.1263946561
0.2%
0.12501870312
0.5%
0.11951489171
0.2%
0.10988322172
0.5%
0.10300345741
0.2%
0.098875598831
0.2%

s2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct302
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.069143212 × 10-17
Minimum-0.115613066
Maximum0.1987879897
Zeros0
Zeros (%)0.0%
Negative239
Negative (%)54.1%
Memory size3.6 KiB
2022-07-07T16:24:35.579022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.115613066
5-th percentile-0.07271172671
Q1-0.03035839726
median-0.003819065121
Q30.02984439452
95-th percentile0.07946276829
Maximum0.1987879897
Range0.3144010556
Interquartile range (IQR)0.06020279178

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)1.170247522 × 1015
Kurtosis0.6013811504
Mean4.069143212 × 10-17
Median Absolute Deviation (MAD)0.0299056781
Skewness0.4365918037
Sum1.771846558 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:35.988881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0010007289645
 
1.1%
0.016222436435
 
1.1%
0.0566185884
 
0.9%
-0.024800012064
 
0.9%
-0.047033552854
 
0.9%
-0.01383981594
 
0.9%
-0.054549115933
 
0.7%
-0.021668527443
 
0.7%
0.0046359433483
 
0.7%
0.037516531843
 
0.7%
Other values (292)404
91.4%
ValueCountFrequency (%)
-0.1156130661
0.2%
-0.11279472981
0.2%
-0.1068449091
0.2%
-0.10433972141
0.2%
-0.10089508831
0.2%
-0.097137306731
0.2%
-0.096197861351
0.2%
-0.095884712891
0.2%
-0.094632119041
0.2%
-0.090561189041
0.2%
ValueCountFrequency (%)
0.19878798971
0.2%
0.15588665041
0.2%
0.13146107041
0.2%
0.13020847651
0.2%
0.12801643731
0.2%
0.12739014041
0.2%
0.12519810111
0.2%
0.11705624111
0.2%
0.11642994421
0.2%
0.10891438111
0.2%

s3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.970291614 × 10-18
Minimum-0.1023070505
Maximum0.1811790604
Zeros0
Zeros (%)0.0%
Negative243
Negative (%)55.0%
Memory size3.6 KiB
2022-07-07T16:24:36.428433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1023070505
5-th percentile-0.06549067248
Q1-0.03511716059
median-0.006584467611
Q30.02931150098
95-th percentile0.07790911999
Maximum0.1811790604
Range0.2834861109
Interquartile range (IQR)0.06442866157

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-6.831715265 × 1015
Kurtosis0.9815074614
Mean-6.970291614 × 10-18
Median Absolute Deviation (MAD)0.03129392133
Skewness0.7992551183
Sum-3.191891196 × 10-15
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:36.854050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0139477432222
 
5.0%
-0.0434008456519
 
4.3%
-0.0397192078518
 
4.1%
-0.00290282980715
 
3.4%
-0.0323559322415
 
3.4%
-0.0213110188315
 
3.4%
0.00814208360515
 
3.4%
-0.0286742944415
 
3.4%
-0.00658446761114
 
3.2%
0.0155053592114
 
3.2%
Other values (53)280
63.3%
ValueCountFrequency (%)
-0.10230705051
 
0.2%
-0.098625412711
 
0.2%
-0.091262137111
 
0.2%
-0.080217223692
 
0.5%
-0.076535585895
1.1%
-0.072853948085
1.1%
-0.069172310287
1.6%
-0.065490672486
1.4%
-0.061809034677
1.6%
-0.058127396878
1.8%
ValueCountFrequency (%)
0.18117906041
 
0.2%
0.17749742261
 
0.2%
0.17381578481
 
0.2%
0.15908923361
 
0.2%
0.1517259581
 
0.2%
0.14068104461
 
0.2%
0.13331776891
 
0.2%
0.12227285552
0.5%
0.11859121773
0.7%
0.10386466651
 
0.2%

s4
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.312543715 × 10-18
Minimum-0.07639450375
Maximum0.1852344433
Zeros0
Zeros (%)0.0%
Negative288
Negative (%)65.2%
Memory size3.6 KiB
2022-07-07T16:24:37.268360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.07639450375
5-th percentile-0.07639450375
Q1-0.03949338287
median-0.002592261998
Q30.03430885888
95-th percentile0.08076737006
Maximum0.1852344433
Range0.261628947
Interquartile range (IQR)0.07380224175

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)-5.728577106 × 1015
Kurtosis0.4444016718
Mean-8.312543715 × 10-18
Median Absolute Deviation (MAD)0.03690112088
Skewness0.7353736479
Sum-2.341876693 × 10-15
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:37.680970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03949338287128
29.0%
-0.002592261998108
24.4%
0.0343088588868
15.4%
0.0712099797533
 
7.5%
-0.0763945037528
 
6.3%
0.108111100613
 
2.9%
0.14501222152
 
0.5%
-0.037648326832
 
0.5%
0.015858298442
 
0.5%
-0.021411833642
 
0.5%
Other values (56)56
12.7%
ValueCountFrequency (%)
-0.0763945037528
 
6.3%
-0.070859335621
 
0.2%
-0.069383290781
 
0.2%
-0.053515808811
 
0.2%
-0.051670752761
 
0.2%
-0.050563719141
 
0.2%
-0.050194707931
 
0.2%
-0.047980640681
 
0.2%
-0.047242618261
 
0.2%
-0.03949338287128
29.0%
ValueCountFrequency (%)
0.18523444331
 
0.2%
0.15534453541
 
0.2%
0.14501222152
 
0.5%
0.14132210941
 
0.2%
0.13025177321
 
0.2%
0.108111100613
2.9%
0.091874607441
 
0.2%
0.086708450521
 
0.2%
0.084863394481
 
0.2%
0.080804271181
 
0.2%

s5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct184
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.243485816 × 10-17
Minimum-0.1260971208
Maximum0.1335972819
Zeros0
Zeros (%)0.0%
Negative230
Negative (%)52.0%
Memory size3.6 KiB
2022-07-07T16:24:38.433777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1260971208
5-th percentile-0.07213275338
Q1-0.03324559265
median-0.001947171087
Q30.03243232416
95-th percentile0.07904814856
Maximum0.1335972819
Range0.2596944028
Interquartile range (IQR)0.0656779168

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)5.151633114 × 1014
Kurtosis-0.1343668243
Mean9.243485816 × 10-17
Median Absolute Deviation (MAD)0.03313977329
Skewness0.2917537293
Sum4.135580767 × 10-14
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:38.843753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0181136923211
 
2.5%
-0.0307479175310
 
2.3%
-0.041176166928
 
1.8%
-0.051403873057
 
1.6%
-0.025953110567
 
1.6%
-0.033245592657
 
1.6%
-0.010903250656
 
1.4%
-0.00061173530466
 
1.4%
-0.061175799056
 
1.4%
-0.02364686316
 
1.4%
Other values (174)368
83.3%
ValueCountFrequency (%)
-0.12609712081
 
0.2%
-0.10436552421
 
0.2%
-0.1016399591
 
0.2%
-0.096434949944
0.9%
-0.093937274831
 
0.2%
-0.089133352251
 
0.2%
-0.086827104792
0.5%
-0.082378690722
0.5%
-0.08023652411
 
0.2%
-0.07813993552
0.5%
ValueCountFrequency (%)
0.13359728192
0.5%
0.13339673871
0.2%
0.13237579121
0.2%
0.13007865931
0.2%
0.12902124941
0.2%
0.12005149641
0.2%
0.11934047941
0.2%
0.10635074571
0.2%
0.10413565431
0.2%
0.10329701881
0.2%

s6
Real number (ℝ)

Distinct56
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.155435727 × 10-17
Minimum-0.1377672257
Maximum0.1356118307
Zeros0
Zeros (%)0.0%
Negative224
Negative (%)50.7%
Memory size3.6 KiB
2022-07-07T16:24:39.288742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.1377672257
5-th percentile-0.07563562197
Q1-0.03317902609
median-0.0010776975
Q30.0279170509
95-th percentile0.0817644408
Maximum0.1356118307
Range0.2733790564
Interquartile range (IQR)0.06109607699

Descriptive statistics

Standard deviation0.04761904762
Coefficient of variation (CV)4.121306491 × 1015
Kurtosis0.2369167379
Mean1.155435727 × 10-17
Median Absolute Deviation (MAD)0.0289947484
Skewness0.2079166162
Sum5.245803791 × 10-15
Variance0.002267573696
MonotonicityNot monotonic
2022-07-07T16:24:39.678883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00306440941422
 
5.0%
0.0196328370720
 
4.5%
0.00720651632920
 
4.5%
-0.001077697519
 
4.3%
-0.0135040182416
 
3.6%
-0.0176461251616
 
3.6%
-0.0383566597315
 
3.4%
-0.0549250873914
 
3.2%
-0.00521980441514
 
3.2%
0.0154907301614
 
3.2%
Other values (46)272
61.5%
ValueCountFrequency (%)
-0.13776722571
 
0.2%
-0.12948301192
 
0.5%
-0.10463037042
 
0.5%
-0.096346156542
 
0.5%
-0.092204049634
0.9%
-0.088061942712
 
0.5%
-0.08391983583
0.7%
-0.079777728884
0.9%
-0.075635621974
0.9%
-0.071493515055
1.1%
ValueCountFrequency (%)
0.13561183073
0.7%
0.13146972382
0.5%
0.12732761691
 
0.2%
0.1190434032
0.5%
0.10661708234
0.9%
0.098332868462
0.5%
0.094190761541
 
0.2%
0.090048654632
0.5%
0.085906547714
0.9%
0.08176444084
0.9%

Interactions

2022-07-07T16:24:26.888917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:01.686118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:04.948926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:08.009058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:11.069030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:14.368835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:17.671281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:20.708592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:23.938661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:27.199241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:02.248835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:05.278799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:08.320894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:11.400916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:14.728934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:18.008693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:21.038490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:24.258664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:27.538512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:02.579298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:05.648793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:08.678408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:12.028646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:15.079072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:18.339559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:21.688185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:24.599230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:27.858780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:02.911838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:05.978278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:09.019451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:12.338542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:15.420859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:18.657890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:21.998432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:24.928635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:28.182019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:03.265373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:06.321839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:09.390684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:12.708761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:15.758740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:19.000116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:22.321607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:25.264632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:28.518592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:03.611467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:06.671058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:09.748604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:13.048743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:16.109091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:19.367226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:22.648883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:25.600187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:28.848768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:03.970582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:07.011747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:10.086151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:13.375682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:16.458868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:19.708481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:22.994423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:25.931436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:29.170908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:04.298419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:07.342754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:10.398929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:13.701730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:16.934719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:20.038153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:23.317203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:26.247386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:29.503757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:04.618155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:07.679083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:10.758802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:14.010219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:17.314424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:20.368475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:23.608673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-07T16:24:26.558872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-07T16:24:40.038455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-07T16:24:40.538877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-07T16:24:41.021750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-07T16:24:41.544521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-07T16:24:30.038344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-07T16:24:30.930314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agesexbmibps1s2s3s4s5s6
00.0380760.0506800.0616960.021872-0.044223-0.034821-0.043401-0.0025920.019907-0.017646
1-0.001882-0.044642-0.051474-0.026328-0.008449-0.0191630.074412-0.039493-0.068332-0.092204
20.0852990.0506800.044451-0.005670-0.045599-0.034194-0.032356-0.0025920.002861-0.025930
3-0.089063-0.044642-0.011595-0.0366560.0121910.024991-0.0360380.0343090.022688-0.009362
40.005383-0.044642-0.0363850.0218720.0039350.0155960.008142-0.002592-0.031988-0.046641
5-0.092695-0.044642-0.040696-0.019442-0.068991-0.0792880.041277-0.076395-0.041176-0.096346
6-0.0454720.050680-0.047163-0.015999-0.040096-0.0248000.000779-0.039493-0.062917-0.038357
70.0635040.050680-0.0018950.0666290.0906200.1089140.0228690.017703-0.0358160.003064
80.0417080.0506800.061696-0.040099-0.0139530.006202-0.028674-0.002592-0.0149600.011349
9-0.070900-0.0446420.039062-0.033213-0.012577-0.034508-0.024993-0.0025920.067737-0.013504

Last rows

agesexbmibps1s2s3s4s5s6
4320.009016-0.0446420.055229-0.0056700.0575970.044719-0.0029030.0232390.0556860.106617
433-0.027310-0.044642-0.060097-0.0297700.0465890.0199800.122273-0.039493-0.051404-0.009362
4340.016281-0.0446420.0013390.0081010.0053110.0108990.030232-0.039493-0.0454240.032059
435-0.012780-0.044642-0.023451-0.040099-0.0167040.004636-0.017629-0.002592-0.038460-0.038357
436-0.056370-0.044642-0.074108-0.050427-0.024960-0.0470340.092820-0.076395-0.061176-0.046641
4370.0417080.0506800.0196620.059744-0.005697-0.002566-0.028674-0.0025920.0311930.007207
438-0.0055150.050680-0.015906-0.0676420.0493410.079165-0.0286740.034309-0.0181140.044485
4390.0417080.050680-0.0159060.017293-0.037344-0.013840-0.024993-0.011080-0.0468830.015491
440-0.045472-0.0446420.0390620.0012150.0163180.015283-0.0286740.0265600.044529-0.025930
441-0.045472-0.044642-0.073030-0.0814130.0837400.0278090.173816-0.039493-0.0042220.003064